Intelligent Scheduling of Robotic Flexible Assembly Cells [electronic resource] /
Abd, Khalid Karam.author.
This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.
Description based upon print version of record.
Parts of this thesis have been published in the following documents:; Journals; International Conferences; Book Chapters; Supervisor's Foreword; Acknowledgments; Contents; Abbreviations; 1 Background and Research Scope; Abstract; 1.1 Introduction; 1.2 Flexible Assembly Systems; 1.2.1 Robots; 1.2.2 Peripheral Equipment; 1.3 Classification of Flexible Assembly Systems; 1.3.1 Robotic Assembly Line; 1.3.2 Robotic Assembly Cell; 1.3.3 Simple Comparison Between RAL and RAC; 1.4 Scheduling of Robotic Flexible Assembly Cell; 1.5 Motivation for Research in Scheduling of RFAC
1.6 Research Gap and Scope1.7 Concluding Remarks; References; 2 Literature Review and Research Objectives; 2.1 Introduction; 2.2 Scheduling Problems in Manufacturing Systems; 2.2.1 Types of Scheduling Problems; 2.2.2 Characteristics of Scheduling Problems; 2.2.3 Solution Approaches; 2.3 Review of Literature on Advanced Scheduling Approaches; 2.3.1 Simulation Approaches; 2.3.2 Artificial Intelligence Approaches; 2.3.3 Observations from the Literature Review; 2.4 Scheduling of RFAC: A Literature Review; 2.4.1 Traditional Approaches; 2.4.2 Simulation Approaches; 2.4.3 Expert System Approaches
2.5 Research Limitations2.6 Research Objectives and Thesis Plan; 2.6.1 Research Objectives; 2.6.2 Research Plan and Thesis Structure; 2.7 Concluding Remarks; References; 3 Development of an Intelligent Methodology for Scheduling RFAC; 3.1 Introduction; 3.2 Application of Fuzzy Logic to Scheduling Problems; 3.3 Proposed Methodology for Scheduling of RFAC; 3.3.1 Pre-processing Module; 3.3.2 Scheduling Module; 3.3.3 Linguistic Variables; 3.3.4 Membership Functions; 3.3.5 Fuzzy Rules; 3.3.5.1 Simulation Module; 3.4 Concluding Remarks; References
4 Case Study 1: Application of the Developed Methodology Using Fuzzy Logic and Simulation4.1 Introduction; 4.2 A Fuzzy Logic Model for Scheduling RFAC; 4.2.1 Defining the Linguistic Variables; 4.2.2 Constructing Membership Functions; 4.2.3 Constructing Fuzzy Rules; 4.3 Implementation of Fuzzy Approach for Scheduling of RFAC; 4.4 Example Application of Scheduling RFAC; 4.4.1 RFAC Description; 4.4.2 Simulation of Experimental Design; 4.5 Simulation Results and Discussion; 4.6 Concluding Remarks; References; 5 Simulation Modelling and Analysis of Dynamic Scheduling in RFAC; 5.1 Introduction
5.2 Review of Literature on Dynamic Events5.3 A Framework for Developing an Intelligent Approach to Dynamic Scheduling Problems; 5.3.1 Preparation; 5.3.2 Application of Taguchi Method; 5.3.3 Simulation Modelling; 5.3.4 Statistical Analysis; 5.4 The Simulation Model; 5.5 Experimental Design and Results; 5.5.1 Experimental Setup; 5.5.2 Taguchi's Orthogonal Array Selection; 5.5.3 Calculation of the Signal-to-Noise (S/N) Ratio; 5.6 Analysis of Results and Discussion; 5.6.1 Analysis of Mean (ANOM); 5.6.2 Analysis of Variance (ANOVA); 5.7 Concluding Remarks; References
6 Development of an Optimization Approach for Dynamic Scheduling Problems in RFAC
Includes bibliographical references and index.
This book focuses on the design of Robotic Flexible Assembly Cell (RFAC) with multi-robots. Its main contribution consists of a new effective strategy for scheduling RFAC in a multi-product assembly environment, in which dynamic status and multi-objective optimization problems occur. The developed strategy, which is based on a combination of advanced solution approaches such as simulation, fuzzy logic, system modeling and the Taguchi optimization method, fills an important knowledge gap in the current literature and paves the way for future research towards the goal of employing flexible assembly systems as effectively as possible despite the complexity of their scheduling.
Computational intelligence.
Manufactures.
Robotics.
Automation.
Artificial intelligence.
Computational Intelligence.
Manufacturing, Machines, Tools, Processes.
Robotics and Automation.
Artificial Intelligence.